Artificial Intelligence (AI) is revolutionizing numerous industries, driving the demand for scalable, efficient and flexible infrastructure to support AI workloads. To function as intended, modern AI applications often require substantial computational resources, scalability and flexibility.

Traditional IT infrastructure was not designed to meet these elevated demands, leading to the rise of and adoption of containerization and orchestration technologies like Kubernetes. Kubernetes is an open-source container orchestration platform initially developed by Google that has evolved into the de facto standard for container orchestration. Kubernetes might be used throughout a typical AI development workflow, including handling data processing tasks, accelerating model training, deploying AI models as a service, built-in monitoring and logging capabilities, and integration with continuous integration and deployment.

In 2016, Google donated the Kubernetes project to the Cloud Native Computing Foundation (CNCF), ensuring that future development and governance of Kubernetes would be managed by the wider open-source community. This transition has allowed Kubernetes to mature into a robust framework for deploying, managing and scaling AI applications efficiently.

This report explores how Kubernetes is currently the preferred platform to address the challenges of AI workloads.